Tracking agitation in people living with dementia in a care environment

TL;DR

This study explores using wearable devices and machine learning to automatically detect agitation episodes in dementia patients, aiming to improve monitoring accuracy and reduce staff workload in care environments.

Contribution

It introduces a real-world multi-modal wearable data collection and a baseline classification model for agitation detection in dementia care.

Findings

Baseline model achieved AUC=0.717

Improved model reached AUC=0.779

Demonstrates potential for automated agitation monitoring

Abstract

Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk. In a long term care (LTC) environment, care staff track and document these symptoms as a way to detect when there has been a change in resident status to assess risk, and to monitor for response to interventions. However, this documentation can be time-consuming, and due to staffing constraints, episodes of agitation may go unobserved. This brings into question the reliability of these assessments, and presents an opportunity for technology to help track and monitor behavioural symptoms in dementia. In this paper, we present the outcomes of a 2 year real-world study performed in a dementia unit, where a multi-modal wearable device was worn by $20$ PwD. In line with a commonly used clinical documentation tool, this large multi-modal time-series data was analyzed to track the presence of episodes of agitation in 8-hour nursing shifts. The development of a baseline classification model (AUC=0.717) on this dataset and subsequent improvement (AUC= 0.779) lays the groundwork for automating the process of annotating agitation events in nursing charts.